Borrowing it
Nothing to install: this file belongs to sorryorc/mcp-codex-worker. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/sorryorc/mcp-codex-worker/main/.claude/skills/worker-proof-and-analysis-toolkit/SKILL.mdgit clone --depth 1 https://github.com/sorryorc/mcp-codex-workerWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/sorryorc/mcp-codex-worker/worker-proof-and-analysis-toolkit)<a href="https://agentmods.dev/skills/sorryorc/mcp-codex-worker/worker-proof-and-analysis-toolkit"><img src="https://agentmods.dev/badge/skills/sorryorc/mcp-codex-worker/worker-proof-and-analysis-toolkit/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/sorryorc/mcp-codex-worker/worker-proof-and-analysis-toolkit"><img src="https://agentmods.dev/badge/skills/sorryorc/mcp-codex-worker/worker-proof-and-analysis-toolkit.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00030 | $0.00221 |
| Opus 5.5 | $0.00012 | $0.00088 |
| Sonnet 5.5 | $0.00006 | $0.00044 |
| Haiku 4.5 | $0.00003 | $0.00022 |
Grade A, and why
worker-proof-and-analysis-toolkit scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
Worker Proof And Analysis Toolkit
Recipes
Hypothesis To Numbers
- State the mechanism.
- Predict which metric changes and by how much.
- Run the smallest controlled comparison.
- Keep negative evidence.
Semantic Failure Audit
- Pick jobs where checks passed but review disagreed.
- Classify root cause: missing context, wrong tool, weak model, or bad acceptance criteria.
- Add a test or episode fixture for the class.
Blocking Risk Review
- Estimate false positive cost.
- Prefer observe mode until measured.
- Promote to warn, then enforce only after regression coverage exists.
When Not To Use
Do not use this for routine feature implementation. Use worker-change-control.
Verification
npm run stats -- --since-minutes=60npm test
Provenance and maintenance
Retire disproven hypotheses in docs or tests so they are not rediscovered.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2mo ago First seen · 41 lines · 30 tokens per session scan A 9f7a16eebcca
worker-proof-and-analysis-toolkit is a skill published in the GitHub repository sorryorc/mcp-codex-worker (2 stars, last pushed 1mo ago), licensed MIT. It adds 30 tokens to every session and 221 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
great-expectations
Define Great Expectations suites and checkpoints to validate datasets and enforce pipeline data-quality checks.
research-engineer
An uncompromising Academic Research Engineer. Operates with absolute scientific rigor, objective criticism, and zero flair. Focuses on theoretical correctness, formal verification, and optimal implementation across any required technology.
test-failure-explorer
Import and query an existing Vitest 3.2 JSON report to explore test failures, file-level errors and original diagnostic evidence.
experiment-audit
A review step that checks whether an experiment's reported results are supported by real data and a sufficiently broad test.
go-testing
Trigger: Go tests, go test coverage, Bubbletea teatest, golden files. Apply focused Go testing patterns.
unity-agent-workflows
Use for AI-assisted Unity work that needs live repo discovery, project-derived routing, runtime-owner proof, runtime-visible output hard stops, runtime numeric proof for repeated visible-output failures, state-step guards, multi-agent scope ownership, modular C#/asmdef safety, UI/scene/visual asset gates, data-first…